AI Product Management

Why This Course

Artificial Intelligence is reshaping product development, enabling smarter, adaptive, and data-driven solutions across industries.

However, managing AI products requires a unique blend of technical understanding, business acumen, and ethical responsibility.

This program provides participants with the skills and frameworks to lead the full lifecycle of AI products — from conception to deployment and continuous improvement.

It emphasizes strategic alignment, cross-functional collaboration, and responsible innovation, preparing participants to drive AI initiatives that deliver measurable business value and user trust.

What You’ll Learn and Practice

By joining this program, you will:

• Understand the fundamentals of AI technologies and their business applications.
• Learn how to define, design, and manage AI products across their lifecycle.
• Master frameworks for data strategy, model evaluation, and stakeholder alignment.
• Develop tools for responsible AI decision-making and ethical governance.
• Gain hands-on experience in creating product roadmaps, KPIs, and success metrics for AI-driven solutions.

The Program Flow

Day 1: Foundations of AI and Product Management

• What makes AI products different from traditional software products?
• Overview of core AI concepts: machine learning, NLP, computer vision, and generative AI.
• Understanding data dependencies and model-driven decision-making.
• The AI product lifecycle — from ideation to post-deployment iteration.
• Workshop: mapping an AI product ecosystem and identifying value opportunities.

Day 2: Defining AI Product Vision and Strategy

• Translating business problems into AI-powered opportunities.
• Setting strategic goals and measurable success outcomes for AI initiatives.
• Aligning product vision with data availability and model feasibility.
• Competitive landscape analysis and identifying market fit.
• Practical exercise: drafting an AI product charter and success statement.

Day 3: Designing and Developing AI Products

• Building cross-functional AI teams (PMs, data scientists, engineers, and designers).
• Managing data pipelines — collection, labeling, quality, and ethics.
• Working with AI models: selection, evaluation, and iteration cycles.
• Understanding model interpretability, performance trade-offs, and constraints.
• Simulation: managing an AI feature rollout using agile product frameworks.

Day 4: Deployment, Evaluation, and Continuous Learning

• Moving from prototype to production — model integration and scaling.
• Evaluating AI system performance: precision, recall, and business KPIs.
• A/B testing, feedback loops, and continuous model improvement.
• Post-deployment monitoring, model drift detection, and LLMOps practices.
• Case study: managing an AI product launch across multiple user segments.

Day 5: Responsible AI, Governance, and Product Leadership

• Ethical AI design principles: fairness, accountability, and transparency.
• Managing AI risks — bias, privacy, and security in deployed systems.
• Governance frameworks and compliance alignment (EU AI Act, NIST RMF).
• Communicating AI value and performance to stakeholders and executives.
• Action workshop: designing a strategic roadmap for your next AI product initiative.

Individual Impact

• Gain a holistic understanding of how to design, build, and scale AI products.
• Strengthen leadership and decision-making in cross-functional AI teams.
• Learn to balance technical feasibility, ethical responsibility, and business outcomes.
• Build confidence in communicating complex AI concepts to non-technical audiences.
• Develop the capability to manage innovation and product success in dynamic markets.

Work Impact

• Accelerate the successful development and deployment of AI-powered products.
• Improve cross-functional collaboration between data, business, and technical teams.
• Ensure ethical and compliant AI product development aligned with regulations.
• Enhance market competitiveness through innovation and intelligent solutions.
• Foster a data-driven, product-led culture across the organization.

Training Methodology

This program emphasizes hands-on learning and real-world application through a blend of strategy, technology, and product leadership.

Learning methods include:

• Case studies from leading AI product companies.
• Group workshops on ideation, roadmap creation, and ethical risk assessment.
• Simulation of agile sprints for AI product feature delivery.
• Interactive discussions on product metrics and stakeholder management.
• Practical toolkits, templates, and product frameworks for immediate application.

Beyond the Course

Upon completion, participants will be equipped to lead AI product management with confidence and strategic vision.

They will leave ready to design, launch, and scale responsible, high-impact AI products — driving innovation, trust, and measurable business growth.